量化交易----利用机器学习预测股票价格趋势

有了股票历史数据，如果我们决定采用机器学习的方法来制定策略算法的话，接下的步骤就是分析数据、选择特征和机器学习模型、预测结果等等。

由于股票的数据分析和特征选择比较多样化，这里我们随意选取股票前两天的价格作为输入特征，当然实际工作中的特征选取就比这要复杂的多了。

#!/usr/bin/python
# -*- coding: utf-8 -*-

# forecast.py

from __future__ import print_function

import datetime
import numpy as np
import pandas as pd
import sklearn

from pandas.io.data import DataReader
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.lda import LDA
from sklearn.metrics import confusion_matrix
from sklearn.qda import QDA
from sklearn.svm import LinearSVC, SVC


def create_lagged_series(symbol, start_date, end_date, lags=5):
    """
    This creates a pandas DataFrame that stores the 
    percentage returns of the adjusted closing value of 
    a stock obtained from Yahoo Finance, along with a 
    number of lagged returns from the prior trading days 
    (lags defaults to 5 days). Trading volume, as well as 
    the Direction from the previous day, are also included.
    """

    # Obtain stock information from Yahoo Finance
    ts = DataReader(
    	symbol, "yahoo", 
    	start_date-datetime.timedelta(days=365), 
    	end_date
    )

    # Create the new lagged DataFrame
    tslag = pd.DataFrame(index=ts.index)
    tslag["Today"] = ts["Adj Close"]
    tslag["Volume"] = ts["Volume"]

    # Create the shifted lag series of prior trading period close values
    for i in range(0, lags):
        tslag["Lag%s" % str(i+1)] = ts["Adj Close"].shift(i+1)

    # Create the returns DataFrame
    tsret = pd.DataFrame(index=tslag.index)
    tsret["Volume"] = tslag["Volume"]
    tsret["Today"] = tslag["Today"].pct_change()*100.0

    # If any of the values of percentage returns equal zero, set them to
    # a small number (stops issues with QDA model in scikit-learn)
    for i,x in enumerate(tsret["Today"]):
        if (abs(x) < 0.0001):
            tsret["Today"][i] = 0.0001

    # Create the lagged percentage returns columns
    for i in range(0, lags):
        tsret["Lag%s" % str(i+1)] = \
        tslag["Lag%s" % str(i+1)].pct_change()*100.0

    # Create the "Direction" column (+1 or -1) indicating an up/down day
    tsret["Direction"] = np.sign(tsret["Today"])
    tsret = tsret[tsret.index >= start_date]

    return tsret


if __name__ == "__main__":
    # Create a lagged series of the S&P500 US stock market index
    snpret = create_lagged_series(
    	"^GSPC", datetime.datetime(2001,1,10), 
    	datetime.datetime(2005,12,31), lags=5
    )

    # Use the prior two days of returns as predictor 
    # values, with direction as the response
    X = snpret[["Lag1","Lag2"]]
    y = snpret["Direction"]

    # The test data is split into two parts: Before and after 1st Jan 2005.
    start_test = datetime.datetime(2005,1,1)

    # Create training and test sets
    X_train = X[X.index < start_test]
    X_test = X[X.index >= start_test]
    y_train = y[y.index < start_test]
    y_test = y[y.index >= start_test]
   
    # Create the (parametrised) models
    print("Hit Rates/Confusion Matrices:\n")
    models = [("LR", LogisticRegression()), 
              ("LDA", LDA()), 
              ("QDA", QDA()),
              ("LSVC", LinearSVC()),
              ("RSVM", SVC(
              	C=1000000.0, cache_size=200, class_weight=None,
                coef0=0.0, degree=3, gamma=0.0001, kernel='rbf',
                max_iter=-1, probability=False, random_state=None,
                shrinking=True, tol=0.001, verbose=False)
              ),
              ("RF", RandomForestClassifier(
              	n_estimators=1000, criterion='gini', 
                max_depth=None, min_samples_split=2, 
                min_samples_leaf=1, max_features='auto', 
                bootstrap=True, oob_score=False, n_jobs=1, 
                random_state=None, verbose=0)
              )]

    # Iterate through the models
    for m in models:
        
        # Train each of the models on the training set
        m[1].fit(X_train, y_train)

        # Make an array of predictions on the test set
        pred = m[1].predict(X_test)

        # Output the hit-rate and the confusion matrix for each model
        print("%s:\n%0.3f" % (m[0], m[1].score(X_test, y_test)))
        print("%s\n" % confusion_matrix(pred, y_test))